National Survey of Family Growth (NSFG)

The principal survey to measure reproductive behavior in the United States population.

• Female and male tables with one row per respondent, and a separate one row per pregnancy table.

• A complex sample survey designed to generalize to the 15-49 year old US population by gender.

• Released every couple of years since 1973.

• Administered by the Centers for Disease Control and Prevention, data collection managed by RTI.

---

Recommended Reading

Four Example Strengths & Limitations:

✔️ Detailed questions about fertility and relationship history

✔️ Face-to-face fieldwork conducted by female interviewers

❌ Abortions under-reported in demographic surveys

❌ Sample sizes too small for state-level or single year estimates

Three Example Findings:

1. One out of five mothers with a college degree had a nonmarital first birth during 2015-2017.

2. The rate of unintended pregnancy in the US declined substantially between 2008 and 2011.

3. During 2022-2023, 36% of females 15-49 received a family planning service in the past 12 months.

Two Methodology Documents:

Public-Use Data File Documentation: 2022-2023 National Survey of Family Growth

Wikipedia Entry

One Haiku:

# family structure
# questions cuz radar fails at
# storks with bassinets

---

Download, Import, Preparation

library(haven)

sas_url <-
    "https://ftp.cdc.gov/pub/Health_Statistics/NCHS/NSFG/NSFG-2022-2023-FemRespPUFData.sas7bdat"
    
nsfg_tbl <- read_sas( sas_url )
    
nsfg_df <- data.frame( nsfg_tbl )

names( nsfg_df ) <- tolower( names( nsfg_df ) )

Save Locally  

Save the object at any point:

# nsfg_fn <- file.path( path.expand( "~" ) , "NSFG" , "this_file.rds" )
# saveRDS( nsfg_df , file = nsfg_fn , compress = FALSE )

Load the same object:

# nsfg_df <- readRDS( nsfg_fn )

Survey Design Definition

Construct a complex sample survey design:

library(survey)

options( survey.lonely.psu = "adjust" )

nsfg_design <- 
    svydesign( 
        id = ~ vecl , 
        strata = ~ vest , 
        data = nsfg_df , 
        weights = ~ wgt2022_2023 , 
        nest = TRUE 
    )

Variable Recoding

Add new columns to the data set:

nsfg_design <- 
    update( 
        nsfg_design , 

        one = 1 ,
        
        birth_control_pill = as.numeric( constat1 == 6 ) ,
        
        age_categories = 
            factor( findInterval( ager , c( 15 , 20 , 25 , 30 , 35 , 40 ) ) ,
                labels = c( '15-19' , '20-24' , '25-29' , '30-34' , '35-39' , '40-49' ) ) ,
        
        marstat =
            factor( marstat , levels = c( 1:6 , 8:9 ) ,
                labels = c(
                    "Married to a person of the opposite sex" ,
                    "Not married but living together with a partner of the opposite sex" ,
                    "Widowed" ,
                    "Divorced or annulled" ,
                    "Separated, because you and your spouse are not getting along" ,
                    "Never been married" ,
                    "Refused" ,
                    "Don't know" )
            )
    )

---

Analysis Examples with the survey library  

Unweighted Counts

Count the unweighted number of records in the survey sample, overall and by groups:

sum( weights( nsfg_design , "sampling" ) != 0 )

svyby( ~ one , ~ age_categories , nsfg_design , unwtd.count )

Weighted Counts

Count the weighted size of the generalizable population, overall and by groups:

svytotal( ~ one , nsfg_design )

svyby( ~ one , ~ age_categories , nsfg_design , svytotal )

Descriptive Statistics

Calculate the mean (average) of a linear variable, overall and by groups:

svymean( ~ pregnum , nsfg_design , na.rm = TRUE )

svyby( ~ pregnum , ~ age_categories , nsfg_design , svymean , na.rm = TRUE )

Calculate the distribution of a categorical variable, overall and by groups:

svymean( ~ marstat , nsfg_design )

svyby( ~ marstat , ~ age_categories , nsfg_design , svymean )

Calculate the sum of a linear variable, overall and by groups:

svytotal( ~ pregnum , nsfg_design , na.rm = TRUE )

svyby( ~ pregnum , ~ age_categories , nsfg_design , svytotal , na.rm = TRUE )

Calculate the weighted sum of a categorical variable, overall and by groups:

svytotal( ~ marstat , nsfg_design )

svyby( ~ marstat , ~ age_categories , nsfg_design , svytotal )

Calculate the median (50th percentile) of a linear variable, overall and by groups:

svyquantile( ~ pregnum , nsfg_design , 0.5 , na.rm = TRUE )

svyby( 
    ~ pregnum , 
    ~ age_categories , 
    nsfg_design , 
    svyquantile , 
    0.5 ,
    ci = TRUE , na.rm = TRUE
)

Estimate a ratio:

svyratio( 
    numerator = ~ pregnum , 
    denominator = ~ lbpregs , 
    nsfg_design ,
    na.rm = TRUE
)

Subsetting

Restrict the survey design to Ever Cohabited with a Non-Marital Male Partner:

sub_nsfg_design <- subset( nsfg_design , evrcohab == 1 )

Calculate the mean (average) of this subset:

svymean( ~ pregnum , sub_nsfg_design , na.rm = TRUE )

Measures of Uncertainty

Extract the coefficient, standard error, confidence interval, and coefficient of variation from any descriptive statistics function result, overall and by groups:

this_result <- svymean( ~ pregnum , nsfg_design , na.rm = TRUE )

coef( this_result )
SE( this_result )
confint( this_result )
cv( this_result )

grouped_result <-
    svyby( 
        ~ pregnum , 
        ~ age_categories , 
        nsfg_design , 
        svymean ,
        na.rm = TRUE 
    )
    
coef( grouped_result )
SE( grouped_result )
confint( grouped_result )
cv( grouped_result )

Calculate the degrees of freedom of any survey design object:

degf( nsfg_design )

Calculate the complex sample survey-adjusted variance of any statistic:

svyvar( ~ pregnum , nsfg_design , na.rm = TRUE )

Include the complex sample design effect in the result for a specific statistic:

# SRS without replacement
svymean( ~ pregnum , nsfg_design , na.rm = TRUE , deff = TRUE )

# SRS with replacement
svymean( ~ pregnum , nsfg_design , na.rm = TRUE , deff = "replace" )

Compute confidence intervals for proportions using methods that may be more accurate near 0 and 1. See ?svyciprop for alternatives:

svyciprop( ~ birth_control_pill , nsfg_design ,
    method = "likelihood" , na.rm = TRUE )

Regression Models and Tests of Association

Perform a design-based t-test:

svyttest( pregnum ~ birth_control_pill , nsfg_design )

Perform a chi-squared test of association for survey data:

svychisq( 
    ~ birth_control_pill + marstat , 
    nsfg_design 
)

Perform a survey-weighted generalized linear model:

glm_result <- 
    svyglm( 
        pregnum ~ birth_control_pill + marstat , 
        nsfg_design 
    )

summary( glm_result )

---

Replication Example

This example matches the Variance Estimates for Percentages using SAS (9.4) and STATA (18):

Match the sum of the weights:

result <- svytotal( ~ one , nsfg_design )

stopifnot( round( coef( result ) , 0 ) == 74936918 )

stopifnot( round( SE( result ) , 0 ) == 2910451 )

Match row percentages of women currently using the pill by age:

row_percents <- c( 14.2348 , 18.9586 , 14.6057 , 10.1973 , 7.8114 , 6.8632 )

std_err_row_percents <- c( 1.6792 , 2.0226 , 1.8889 , 1.3836 , 1.1050 , 0.7961 )

results <- svyby( ~ birth_control_pill , ~ age_categories , nsfg_design , svymean )

stopifnot( all( round( coef( results ) * 100 , 4 ) == row_percents ) )

stopifnot( all( round( SE( results ) * 100 , 4 ) == std_err_row_percents ) )

---

Analysis Examples with srvyr  

The R srvyr library calculates summary statistics from survey data, such as the mean, total or quantile using dplyr-like syntax. srvyr allows for the use of many verbs, such as summarize, group_by, and mutate, the convenience of pipe-able functions, the tidyverse style of non-standard evaluation and more consistent return types than the survey package. This vignette details the available features. As a starting point for NSFG users, this code replicates previously-presented examples:

library(srvyr)
nsfg_srvyr_design <- as_survey( nsfg_design )

Calculate the mean (average) of a linear variable, overall and by groups:

nsfg_srvyr_design %>%
    summarize( mean = survey_mean( pregnum , na.rm = TRUE ) )

nsfg_srvyr_design %>%
    group_by( age_categories ) %>%
    summarize( mean = survey_mean( pregnum , na.rm = TRUE ) )